Electricity market risk management using forward contracts with bilateral options

Chung, T.S.; Zhang, Shaohua; Yu, C.W.; Wong, Kit Po

doi:10.1049/ip-gtd:20030532

Cited by 48 publications

(29 citation statements)

References 13 publications

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“…A literature review of derivatives in electricity markets [13][14][15][16] reveals that, large consumers who need a fixed price for electricity can resort to future, forward contracts or options to remove really high peak prices that could reduce their operating margins; on the other hand, retailers or aggregators can settle options to mitigate consumers load deviations or significant forecasting errors that yield economic losses.…”

Section: Financial Product Descriptionmentioning

confidence: 99%

Valuation of an American Option for the Spanish Secondary Reserve Market Using a Machine Learning Model

Malpica

Frías

2019

IEEE Trans. Power Syst.

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Abstract-This paper presents an original methodology to compute a financial product that could enhance the demand side participation in ancillary services, specially for industrial consumers. The financial product consists in an american option on the Spanish secondary reserve market for the following day, where the buyer has the right but not the obligation to offer part of its capacity to the system operator. Considering this approach, an industrial consumer would receive an economic incentive to offer its flexibility to the system without changing its production planning, paying an upfront premium. The computation of the american option is leveraged on a Monte Carlo simulation approach where the random paths are obtained from a machine learning model. The machine learning model attempts to forecast the 24-hour secondary band prices of the following day using a combination of different algorithms; the output of the model is used as a baseline to perform the Monte Carlo simulation that computes the option value. LIST OF SYMBOLSt, τ time -hour of the day d day T tenor of the american option, i.e. 24 hours i index of the random path I total number of random paths g index of basis function G total number of basis functions β r coefficient vector of ridge regression γ shrinkage parameter of ridge penalty K strike -electricity valuation α g,t coefficient of basis function g at hour t S t secondary band price prediction at hour t S t secondary band price at hour t Y t error term at hour t X t feature vector at hour t Z t logarithm of the error term at hour t W t input feature vector of the second layer at hour t V t option value at hour t V t,i option value at hour t for random path i V t+∆t,i simulated continuation value at hour t for random path i h t payoff of the option at hour t S t,i secondary band price at hour t for random path i C t continuation value at hour t C t,i estimation of the continuation value at hour t for random path i

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Section: Financial Product Descriptionmentioning

confidence: 99%

Valuation of an American Option for the Spanish Secondary Reserve Market Using a Machine Learning Model

Malpica

Frías

2019

IEEE Trans. Power Syst.

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“…En este contexto, los resultados que se han obtenido de los modelos desarrollados pueden servir como datos de entrada a modelos de valoración de contratos más sofisticados como los planteados por [2], [16] y [17].…”

Section: B Conclusiones De La Valoración De Contratosunclassified

Valoración de contratos a plazo en mercados eléctricos: Aplicación al mercado ecuatoriano

Quizhpe¹,

Baı́llo²,

Ventós³

2008

ings

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Los mercados de energía eléctrica se caracterizan por la extremada volatilidad del precio spot. La incertidumbre asociada al precio es una fuente de riesgo tanto para los agentes vendedores (compañías de generación) como para los agentes compradores. Por este motivo, se hace necesario desarrollar herramientas y metodologías de análisis, valoración y gestión del riesgo asociado al negocio de generación. En este documento se propone, formula y desarrolla un procedimiento para la valoración de contratos mayoristas de electricidad a largo plazo con el objetivo de lograr un adecuado equilibrio entre riesgo y rentabilidad en el contexto de las empresas de generación que operan en el mercado eléctrico ecuatoriano.

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“…Reference [17] discusses the economics of electricity hedging using future contracts. Reference [18] deals with the design of forward contracts bundled with financial options for electricity risk management. A statistical study of direct and cross hedging using future contracts is given in [9].…”

Section: Introductionmentioning

confidence: 99%

Optimal Portfolio Selection of GenCo Under Congestion Risk in Multimarket Environment

Panda

Singh

2017

Technol Econ Smart Grids Sustain Energy

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In a restructured electricity market derivative instruments help in reducing price risk. This paper proposes a portfolio optimization model considering uncertainty of electricity spot prices. Using this model, a thermal generation company (GenCo) holding contracts within and outside its jurisdiction can maximize its profit with limited risk exposure. The proposed model is formulated using mean variance portfolio theory, considering spot market, bilateral contracts and options with the possibility of managing congestion risk while trading between different locations. To handle uncertainties scenario generation and reduction techniques are used. The spot price inaccuracies are further represented as scenario tree. The producers risk preference is expressed by a utility function as the trade-off between expectation and variance of the return. For multiple scenarios the optimization problem is solved to obtain a stochastic solution to the optimal capacity allocation problem. The work addresses price variability of zonal transaction considering both physical and financial contracts to schedule the output of an opportunistic GenCo for maximizing its profit by creating hedging strategy through multiple scenarios. The results indicate that the proposed model is capable of improving the profit risk trade-off of the portfolios. GenCos profit seeking behavior during congestion relieving situation has also been demonstrated in the proposed Debasmita Panda

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Electricity market risk management using forward contracts with bilateral options

Cited by 48 publications

References 13 publications

Valuation of an American Option for the Spanish Secondary Reserve Market Using a Machine Learning Model

Valuation of an American Option for the Spanish Secondary Reserve Market Using a Machine Learning Model

Valoración de contratos a plazo en mercados eléctricos: Aplicación al mercado ecuatoriano

Optimal Portfolio Selection of GenCo Under Congestion Risk in Multimarket Environment

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